Jeff Z. Pan


KEViN: A Knowledge Enhanced Validity and Novelty Classifier for Arguments
Ameer Saadat-Yazdi | Xue Li | Sandrine Chausson | Vaishak Belle | Björn Ross | Jeff Z. Pan | Nadin Kökciyan
Proceedings of the 9th Workshop on Argument Mining

The ArgMining 2022 Shared Task is concerned with predicting the validity and novelty of an inference for a given premise and conclusion pair. We propose two feed-forward network based models (KEViN1 and KEViN2), which combine features generated from several pretrained transformers and the WikiData knowledge graph. The transformers are used to predict entailment and semantic similarity, while WikiData is used to provide a semantic measure between concepts in the premise-conclusion pair. Our proposed models show significant improvement over RoBERTa, with KEViN1 outperforming KEViN2 and obtaining second rank on both subtasks (A and B) of the ArgMining 2022 Shared Task.


A Knowledge-Guided Framework for Frame Identification
Xuefeng Su | Ru Li | Xiaoli Li | Jeff Z. Pan | Hu Zhang | Qinghua Chai | Xiaoqi Han
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Frame Identification (FI) is a fundamental and challenging task in frame semantic parsing. The task aims to find the exact frame evoked by a target word in a given sentence. It is generally regarded as a classification task in existing work, where frames are treated as discrete labels or represented using onehot embeddings. However, the valuable knowledge about frames is neglected. In this paper, we propose a Knowledge-Guided Frame Identification framework (KGFI) that integrates three types frame knowledge, including frame definitions, frame elements and frame-to-frame relations, to learn better frame representation, which guides the KGFI to jointly map target words and frames into the same embedding space and subsequently identify the best frame by calculating the dot-product similarity scores between the target word embedding and all of the frame embeddings. The extensive experimental results demonstrate KGFI significantly outperforms the state-of-the-art methods on two benchmark datasets.


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Ontology Authoring Inspired By Dialogue
Artemis Parvizi | Yuan Ren | Markel Vigo | Kees van Deemter | Chris Mellish | Jeff Z. Pan | Robert Stevens | Caroline Jay
Proceedings of the 1st Workshop on Language and Ontologies

When is Lying the Right Choice?
Federico Cerutti | Artemis Parvizi | Alice Toniolo | Dave Braines | Geeth R. de Mel | Timothy J. Norman | Nir Oren | Jeff Z. Pan | Gavin Pearson | Stephen D. Pipes | Paul Sullivan
Proceedings of the 1st Workshop on Language and Ontologies


A Pilot Experiment in Knowledge Authoring as Dialogue
Artemis Parvizi | Caroline Jay | Christopher Mellish | Jeff Z. Pan | Yuan Ren | Robert Stevens | Kees van Deemter
Proceedings of the 10th International Conference on Computational Semantics (IWCS 2013) – Short Papers

Transfer Learning Based Cross-lingual Knowledge Extraction for Wikipedia
Zhigang Wang | Zhixing Li | Juanzi Li | Jie Tang | Jeff Z. Pan
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)


Charting the Potential of Description Logic for the Generation of Referring Expressions
Yuan Ren | Kees van Deemter | Jeff Z. Pan
Proceedings of the 6th International Natural Language Generation Conference